Interactive Layout Drawing Interface with Shadow Guidance

It is difficult to design a visually appealing layout for common users, which takes time even for professional designers. In this paper, we present an interactive layout design system with shadow guidance and layout retrieval to help users obtain satisfactory design results. This study focuses in particular on the design of academic presentation slides. The user may refer to the shadow guidance as a heat map, which is the layout distribution of our gathered data set, using the suggested shadow guidance. The suggested system is data-driven, allowing users to analyze the design data naturally. The layout may then be edited by the user to finalize the layout design. We validated the suggested interface in our user study by comparing it with common design interfaces. The findings show that the suggested interface may achieve high retrieval accuracy while simultaneously offering a pleasant user experience.Comment: 6 pages, 7 figures, accepted in IWAIT2023, video is here https://youtu.be/Rddjz5jloJ

Identification of individual slit valves in semiconductor manufacturing fab based on their vibration signatures

textSlit valves play an important role in semiconductor manufacturing industry. They enable creation of a vacuum environment required for wafer processing. Due to the high volume of production in the modern semiconductor industry, slit valves experience severe degradation over their useful lifetime. If maintenance is not applied in due time, degraded valves may lead to defects in end products because of pressure loss and particle generation. In this thesis, we proposed methods for signal processing and feature extraction for analysis of slit valve vibration signatures. These methods would be used to demonstrate the ability of reliably, accurately and efficiently distinguish between each individual valve via a multi-class classification procedure. Such ability is a clear illustration of the feasibility of vibration based monitoring of the slit valve conditions.Mechanical Engineerin

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients

Drug prescription support in dental clinics through drug corpus mining

The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug pairs, by using feature vectors generated from term similarities and word embeddings of biomedical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model’s association of the similarity ratio between two drugs yielded a superior F score of 89%. Hence, such an approach, when integrated within the clinical work flow, will reduce prescription errors and thereby increase the health outcomes of patients